1. Experiment Health for: PD LFQ

The experiment contains 12 samples; the condition of interest has 4 categories: sample1, sample2, sample3, sample4.

A.Dimensionality reduction

The data have been log2 transformed and imputed using the MNAR imputation method before PCA.

PCA

MDS

B. Quantitative values CV distributions

2. Intensity distribution across runs

Imputed data

Initial data

3. Feature completedness

By sample

By protein

4. Imputed versus non imputed log2 Intensity values

5. Model QC

Volcano plots

MA plot

Histogram of pvalues

## $breaks
##  [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
## 
## $counts
##  [1] 198 223 293 295 304 273 335 258 295 303
## 
## $density
##  [1] 0.7129996 0.8030248 1.0550954 1.0622974 1.0947065 0.9830753 1.2063378 0.9290601 1.0622974 1.0911055
## 
## $mids
##  [1] 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
## 
## $xname
## [1] "stats[, pval]"
## 
## $equidist
## [1] TRUE
## 
## attr(,"class")
## [1] "histogram"

## $breaks
##  [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
## 
## $counts
##  [1] 221 247 326 313 266 276 286 294 268 306
## 
## $density
##  [1] 0.7884410 0.8811987 1.1630396 1.1166607 0.9489832 0.9846593 1.0203354 1.0488762 0.9561184 1.0916875
## 
## $mids
##  [1] 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
## 
## $xname
## [1] "stats[, pval]"
## 
## $equidist
## [1] TRUE
## 
## attr(,"class")
## [1] "histogram"

## $breaks
##  [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
## 
## $counts
##  [1] 414 391 359 317 249 227 198 220 208 182
## 
## $density
##  [1] 1.4972875 1.4141049 1.2983725 1.1464738 0.9005425 0.8209765 0.7160940 0.7956600 0.7522604 0.6582278
## 
## $mids
##  [1] 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
## 
## $xname
## [1] "stats[, pval]"
## 
## $equidist
## [1] TRUE
## 
## attr(,"class")
## [1] "histogram"

## $breaks
##  [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
## 
## $counts
##  [1] 166 193 265 271 261 295 299 323 332 344
## 
## $density
##  [1] 0.6038559 0.7020735 0.9639869 0.9858130 0.9494362 1.0731175 1.0876682 1.1749727 1.2077119 1.2513641
## 
## $mids
##  [1] 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
## 
## $xname
## [1] "stats[, pval]"
## 
## $equidist
## [1] TRUE
## 
## attr(,"class")
## [1] "histogram"

## $breaks
##  [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
## 
## $counts
##  [1] 269 272 289 295 276 264 264 262 250 279
## 
## $density
##  [1] 0.9889706 1.0000000 1.0625000 1.0845588 1.0147059 0.9705882 0.9705882 0.9632353 0.9191176 1.0257353
## 
## $mids
##  [1] 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
## 
## $xname
## [1] "stats[, pval]"
## 
## $equidist
## [1] TRUE
## 
## attr(,"class")
## [1] "histogram"

## $breaks
##  [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
## 
## $counts
##  [1] 280 291 321 285 283 256 245 257 260 248
## 
## $density
##  [1] 1.0271460 1.0674982 1.1775495 1.0454879 1.0381511 0.9391049 0.8987528 0.9427733 0.9537784 0.9097579
## 
## $mids
##  [1] 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
## 
## $xname
## [1] "stats[, pval]"
## 
## $equidist
## [1] TRUE
## 
## attr(,"class")
## [1] "histogram"